Safe and Sound AI

Introducing Fiddler Guardrails: The Fastest in the Industry

In this episode, we explore how Fiddler Guardrails helps organizations keep large language models (LLMs) on track by moderating prompts and responses before they can cause damage. We break down its industry best latency, secure deployment options, and how it works with Fiddler’s AI observability platform to provide the visibility and control to adapt to evolving threats.
Read the article to learn more about how Fiddler Guardrails can help safeguard your LLM Applications.

Should you Observe ML Metrics or Inferences?

In this episode, we explore two key approaches for monitoring AI models: metrics and inference observation. We break down their trade-offs and provide real-world examples from various industries to illustrate the advantages of each model monitoring strategy for driving responsible AI development. 
Read the article by Fiddler AI and explore additional resources for more information on how AI observability can help developers build trust into AI services.

Tracking Drift to Monitor LLM Performance

In this episode, we discuss how to monitor the performance of Large Language Models (LLMs) in production environments. We explore common enterprise approaches to LLM deployment and evaluate the importance of monitoring for LLM quality or the quality of LLM responses over time. We discuss strategies for "drift monitoring" — tracking changes in both input prompts and output responses — allowing for proactive troubleshooting and improvement via techniques like fine-tuning or augmenting data sources.
Read the article by Fiddler AI and explore additional resources on how AI observability can help developers build trust into AI services.